class_docstr: Implementation of methods in Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.
  Wang et al. (2019).
  
  | Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
class_docstr_tok: ["Implementation","of","methods","in","Neural","Cleanse",":","Identifying","and","Mitigating","Backdoor","Attacks","in","Neural","Networks",".","Wang","et","al",".","(","2019",")",".","|","Paper","link",":","https",":","\/\/people.cs.uchicago.edu\/~ravenben\/publications\/pdf\/backdoor-sp19.pdf"]
docstr: Check if supposed backdoors are effective against the classifier
  
  :param backdoor_data: data with the backdoor added
  :param backdoor_labels: the correct label for the data
  :return: true if any of the backdoors are effective on the model
docstr_tok: ["Check","if","supposed","backdoors","are","effective","against","the","classifier",":","param","backdoor_data",":","data","with","the","backdoor","added",":","param","backdoor_labels",":","the","correct","label","for","the","data",":","return",":","true","if","any","of","the","backdoors","are","effective","on","the","model"]
code: def check_backdoor_effective(
      self, backdoor_data: np.ndarray, backdoor_labels: np.ndarray
  ) -> bool:
      """
      Check if supposed backdoors are effective against the classifier
  
      :param backdoor_data: data with the backdoor added
      :param backdoor_labels: the correct label for the data
      :return: true if any of the backdoors are effective on the model
      """
      backdoor_predictions = self._predict_classifier(backdoor_data)
      backdoor_effective = np.logical_not(
          np.all(backdoor_predictions == backdoor_labels, axis=1)
      )
      return np.any(backdoor_effective)
  
file_path: adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/neural_cleanse.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Union, Tuple, List
  import numpy
  from art.estimators.certification.abstain import AbstainPredictorMixin
  from art.utils import to_categorical
label_desc: Use image node_id 8 for calling the NeuralCleanseMixin obj's underlying member method code with example usage: obj.check_backdoor_effective(backdoor_data, backdoor_labels) and returns: np